4 minute read 12 Jan 2018
Colleagues discussing graphs screen meeting room

How to move your data strategy from insight to implementation


EY Global

Multidisciplinary professional services organization

4 minute read 12 Jan 2018

Knowing how and when to execute a data strategy is often as important as the data itself.

With a broad range of analytics tools at their disposal, and a growing cultural awareness of the centrality of good analytics practices to business success, today’s executives should be more prepared than ever to create a strong, data-led business.

But developing a data strategy and making use of data insights is only half the battle. Actually implementing a successful strategy means more than just giving your sales team a printout of graphs, tables and pie charts and telling them to get on with it. Rather, it requires a careful consideration of what, when and how to implement this data for the best strategy-aligned results, so that rather than just using the data as an optional extra, organizations can infuse the entirety of their operations with value-creating big-picture insights.

Keep your eye on the prize

The first thing to do when transitioning to the implementation phase is to determine what business outcome you want to achieve on the back of your data strategy.

If you have a working hypothesis, you’ll quickly see where differences arise so you can adjust how you design the initiative.
Beatriz Sanz Sáiz
EY Global Consulting Data and Analytics Leader

“If you launch a new advanced analytics initiative, are you expecting to increase the average revenue per customer? Or gain more new customers? Reduce the cost to serve a customer? Something else?” asks Beatriz Sanz Saiz, EY Global Consulting Data & Analytics Leader. “Your assumptions on how to achieve that objective may not always be right in the early stages of an initiative, but if you have a working hypothesis you’ll quickly see where differences arise so you can adjust how you design the initiative.”

Pick the right moment to strike

Once businesses have determined where they want to focus their analytics efforts, the next decision is when to apply them.

Generally speaking, the earlier you move the better. That way, the organization can try out strategies, see what works and amend those strategies accordingly.

This move-fast approach is the strategy most often taken by organizations seen as leading in the analytics space (according to EY and Forbes research, 7% of all 1,500 surveyed organizations). Of these organizations, 38% deploy their insights when they are designing business initiatives. This is often because they already have the relevant data at their disposal. Less mature organizations often have to collate the data as they go on.

Making the most of your data

There are many challenges standing in the way of efficient implementation of data insights, but topping the list, according to the EY and Forbes research, are a lack of human skills and inadequate business processes: 36% and 35% of all organizations, respectively, rate these as major obstacles.

Both of these problems can be addressed by making sure relevant teams are on the same page when it comes to data strategies. 

A critical success factor for improving collaboration is colocation — putting various stakeholders into the same room.
Brenda Niehaus
Group CIO of Standard Bank, based in Johannesburg, South Africa, High Stakes, High Reward

Testing, learning, adapting and evolving

So your data-driven business strategy is up and running, but that’s not the end of the story: organizations also must be able to measure and track the impact of that strategy on their business objectives and amend it accordingly. “Success … means changing the way we make decisions and enable ongoing improvements because we’re putting more information in the hands of business decision-makers,” says Gina Papush, Chief Data and Analytics Officer at QBE Insurance Group.1

Having data is not enough. Analyzing data is not enough. Without a clear focus on the business objectives, even having a data strategy may not be enough.

An ability to correlate analytics programs with the success or failure of outcomes is another distinguishing factor between best-in-class organizations and the rest. Of leading organizations, only 2% reported a lack of visibility into the outcomes of their analytics programs. This figure rises to 18% for “lagging” organizations (about 10% of the total).

Being transparent about the metrics on your data programs can be a significant help in driving reciprocal improvements in operations, by creating an environment where successes are known — and by encouraging shifts in strategy that were taken for the right reasons.

From data collection to data strategy

Having data is not enough. Analyzing data is not enough. Without a clear focus on the business objectives, even having a data strategy may not be enough.

As with any good strategy, knowing when and how to apply it, when to change it, and how to measure its success are all critical for organizations to move from being ones that do analytics, to being truly analytics-driven.


A data strategy that yields value-creating insights begins with a working hypothesis and includes the ability to correlate results with the success or failure of outcomes.

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EY Global

Multidisciplinary professional services organization